A study on relationships between critical success factors of knowledge management and competitive advantage
Bibliographic record
Abstract
This paper discusses the relationships between Critical Success Factors (CSF) of knowledge Management (KM) with Competitive Advantage (CA) in automotive industry (Saipa corporate in IRAN). In this research, four categories were used including Human-Oriented factors, Organization, Technology and Management process and their relevant component as independent variables. The research method is based on a descriptive-survey research. The questionnaire includes all CEO and board of director of all firms who worked for Saipa Co, covering 88 companies with 160 managers. To test the hypotheses, SPSS and LISREL software packages were used. For data analysis, descriptive statistics and inferential statistical tests (structural equation modeling, Pearson correlation coefficient) were used. Results taken from structural equation modeling (SEM) proposed measurement model fit and construct validity. Pearson correlation shows there was meaningful relationship between four categories of CSF of KM and CA when the level of significance was 0.001.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".